Parse multi-format RAG inputs with OmniParse
Run OmniParse locally or as an API service to turn documents, images, audio, video, and web pages into structured Markdown-ready inputs for agent and RAG workflows.
npx skills add agentskillexchange/skills --skill parse-multi-format-rag-inputs-with-omniparse
Use OmniParse when an agent needs repeatable ingestion of mixed unstructured inputs before retrieval, extraction, summarization, or fine-tuning. The operator clones the repo, prepares the Linux/Python environment, runs the local parser service or UI, submits files or URLs through the documented endpoints, and passes the structured Markdown outputs to downstream GenAI workflows. The scope is bounded to parsing and normalization of documents, media, and web content for agent ingestion; it is not a generic platform card, model framework, or one-off file conversion utility.
What this skill actually does
Inputs and prerequisites: Linux, Python 3.10, Conda or virtualenv, Docker optional, GPU recommended for heavier parsing workloads.
Setup notes: Clone `https://github.com/adithya-s-k/omniparse`, create a Python 3.10 environment, install the project dependencies from the repository instructions, then run the local OmniParse service/UI and submit documents, media, or web URLs through the documented parsing endpoints.
Source and verification boundary: use https://omniparse.cognitivelab.in/ as the canonical reference before running the workflow; keep commands, API calls, CLI usage, and generated outputs reviewable against that upstream source.
Framework fit: publish this as a Multi-Framework workflow only when the operator can invoke the documented toolchain directly, rather than treating the upstream project as a generic product listing.